Breast Cancer Classification From Histological Images with Multiple Features and Random Subspace Classifier Ensemble
Yungang Zhang, Bailing Zhang, Wenjin Lu, Tuan D. Pham, Xiaobo Zhou, Hiroshi Tanaka, Mayumi Oyama-Higa, Xiaoyi Jiang, Changming Sun, Jeanne Kowalski, Xiuping Jia · AIP conference proceedings · 2011
Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co‐occurence Matrix (GLCM) and Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.